code: def vertex_forward(self, idx, pose=None, beta=None):
      if pose is None:
          pose = self.poses[idx][None]
      if beta is None:
          beta = self.betas[idx][None]
      _, T_t2pose = self.body_model.verts_transformations(
          return_tensor=True,
          poses=pose,
          betas=beta,
          transl=None,
      )
      _, T_t2da = self.body_model.verts_transformations(
          return_tensor=True,
          poses=self.da_smpl,
          betas=beta,
          transl=None,
      )
      T_da2pose = T_t2pose @ torch.inverse(T_t2da)
      T_da2scene = self.alignments[idx].T @ T_da2pose
      s = torch.eye(4).to(T_da2scene.device)
      s[:3, :3] *= self.scale
      T_da2scene = s @ T_da2scene
      da_pose_verts = self.body_model(
          return_tensor=True,
          return_joints=False,
          poses=self.da_smpl,
          betas=beta,
          transl=None,
      )
      world_verts = torch.einsum(
          "bni, bi->bn",
          T_da2scene[0],
          ray_utils.to_homogeneous(da_pose_verts),
      )[:, :3][None]
      return world_verts, T_da2scene
  
file_path: ml-neuman/models/human_nerf.py
imports: import os
  import copy
  import torch
  import torch.nn
  from utils import utils, ray_utils
  from models import vanilla
  from models.smpl import SMPL
label_desc: Use image node_id 2 for calling the HumanNeRF obj's underlying member method code with example usage: obj.vertex_forward(idx, pose, beta) and returns: world_verts,  T_da2scene